Benefits of deep learning classification of continuous noninvasive brain–computer interface control

Stieger, James; Engel, Stephen A.; Suma, Daniel; He, Bin

doi:10.1088/1741-2552/ac0584

Cited by 33 publications

(28 citation statements)

References 94 publications

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“…Furthermore, classification scores of low performing users can be improved by incorporating new AI algorithms such as deep learning methods on raw EEG signals instead of the classical machine learning approach that relies on EEG feature extraction (e.g., Stieger et al, 2021;Tibrewal et al, 2021;Zhang et al, 2021). Deep learning models have the advantage of facilitating end-toend learning; they can exploit information from raw data on their own, which is not only computationally more effective but also captures brain activity patterns underlying MI beyond the defined ERD features (Tibrewal et al, 2021).…”

Section: Future Researchmentioning

confidence: 99%

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Leeuwis

Yoon

Alimardani

2021

Front. Hum. Neurosci.

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Motor Imagery BCI systems have a high rate of users that are not capable of modulating their brain activity accurately enough to communicate with the system. Several studies have identified psychological, cognitive, and neurophysiological measures that might explain this MI-BCI inefficiency. Traditional research had focused on mu suppression in the sensorimotor area in order to classify imagery, but this does not reflect the true dynamics that underlie motor imagery. Functional connectivity reflects the interaction between brain regions during the MI task and resting-state network and is a promising tool in improving MI-BCI classification. In this study, 54 novice MI-BCI users were split into two groups based on their accuracy and their functional connectivity was compared in three network scales (Global, Large and Local scale) during the resting-state, left vs. right-hand motor imagery task, and the transition between the two phases. Our comparison of High and Low BCI performers showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings contribute to the existing literature that indeed connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.

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Section: Future Researchmentioning

confidence: 99%

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Leeuwis

Yoon

Alimardani

2021

Front. Hum. Neurosci.

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“…BCI inefficiency is a significant problem that warrants research effort if these systems are to be useful in the future (Maskeliunas et al, 2016). Recent studies have tried to improve the classification performance of BCI inefficiency subjects using the deep learning method (i.e., convolutional neural network) because they cannot produce stronger contralateral ERD/ERS activity (Zhang et al, 2019b;Stieger et al, 2021;Tibrewal et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users

Park

Kim

et al. 2021

Front. Neurosci.

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The motor imagery (MI)-based brain-computer interface (BCI) is an intuitive interface that provides control over computer applications directly from brain activity. However, it has shown poor performance compared to other BCI systems such as P300 and SSVEP BCI. Thus, this study aimed to improve MI-BCI performance by training participants in MI with the help of sensory inputs from tangible objects (i.e., hard and rough balls), with a focus on poorly performing users. The proposed method is a hybrid of training and imagery, combining motor execution and somatosensory sensation from a ball-type stimulus. Fourteen healthy participants participated in the somatosensory-motor imagery (SMI) experiments (within-subject design) involving EEG data classification with a three-class system (signaling with left hand, right hand, or right foot). In the scenario of controlling a remote robot to move it to the target point, the participants performed MI when faced with a three-way intersection. The SMI condition had a better classification performance than did the MI condition, achieving a 68.88% classification performance averaged over all participants, which was 6.59% larger than that in the MI condition (p < 0.05). In poor performers, the classification performance in SMI was 10.73% larger than in the MI condition (62.18% vs. 51.45%). However, good performers showed a slight performance decrement (0.86%) in the SMI condition compared to the MI condition (80.93% vs. 81.79%). Combining the brain signals from the motor and somatosensory cortex, the proposed hybrid MI-BCI system demonstrated improved classification performance, this phenomenon was predominant in poor performers (eight out of nine subjects). Hybrid MI-BCI systems may significantly contribute to reducing the proportion of BCI-inefficiency users and closing the performance gap with other BCI systems.

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“…Over the past decades, there have been lots of BCI studies. They were usually focused on the methods to improve the prediction accuracy [5,8,23,[30][31][32], raise the number of commands [12,33], increase the information transfer rate (ITR) [34][35][36][37][38], or reduce the training efforts [7,30,34,39]. To enhance the prediction accuracy, new classification algorithms [30,40,41] or feature extraction methods have been proposed [31,32,42].…”

Section: Discussionmentioning

confidence: 99%

“…The motor cortex's alpha wave (8~13 Hz) and beta wave (13~30 Hz) will increase or decrease according to the movement intention. For example, when users want to move their hands or feet, the power of alpha and beta waves decreases on the corresponding motor cortex [2,[5][6][7][8]. Therefore, the BCI system can predict a left hand, a right hand, or feet movement intention using power change of the brain area.…”

Section: Introductionmentioning

confidence: 99%

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Studies to Overcome Brain–Computer Interface Challenges

Choi

Yeom

2022

Applied Sciences

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A brain–computer interface (BCI) is a promising technology that can analyze brain signals and control a robot or computer according to a user’s intention. This paper introduces our studies to overcome the challenges of using BCIs in daily life. There are several methods to implement BCIs, such as sensorimotor rhythms (SMR), P300, and steady-state visually evoked potential (SSVEP). These methods have different pros and cons according to the BCI type. However, all these methods are limited in choice. Controlling the robot arm according to the intention enables BCI users can do various things. We introduced the study predicting three-dimensional arm movement using a non-invasive method. Moreover, the study was described compensating the prediction using an external camera for high accuracy. For daily use, BCI users should be able to turn on or off the BCI system because of the prediction error. The users should also be able to change the BCI mode to the efficient BCI type. The BCI mode can be transformed based on the user state. Our study was explained estimating a user state based on a brain’s functional connectivity and a convolutional neural network (CNN). Additionally, BCI users should be able to do various tasks, such as carrying an object, walking, or talking simultaneously. A multi-function BCI study was described to predict multiple intentions simultaneously through a single classification model. Finally, we suggest our view for the future direction of BCI study. Although there are still many limitations when using BCI in daily life, we hope that our studies will be a foundation for developing a practical BCI system.

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Benefits of deep learning classification of continuous noninvasive brain–computer interface control

Cited by 33 publications

References 94 publications

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users

Studies to Overcome Brain–Computer Interface Challenges

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